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Моделирование изменения цены биржевого инструмента на базе микроструктурных рыночных данных // Modeling Stock Price Changes Based on Microstructural Market Data

Author

Listed:
  • N. Bilev A.

    (Lomonosov Moscow State university, Mosocow)

  • Н. Билев А.

    (Московский государственный университет им. М. В. Ломоносова, Москва)

Abstract

In modern electronic stock exchanges there is an opportunity to analyze event driven market microstructure data. This data is highly informative and describes physical price formation which makes it possible to find complex patterns in price dynamics. It is very time consuming and hard to find this kind of patterns by handcrafted rules. However, modern machine learning models are able to solve such issues automatically by learning price behavior which is always changing. The present study presents profitable trading system based on a machine learning model and market microstructure data. Data for the research was collected from Moscow stock exchange MICEX and represents a limit order book change log and all market trades of a liquid security for a certain period. Logistic regression model was used and compared to neural network models with different configuration. According to the study results logistic regression model has almost the same prediction quality as neural network models have but also has a high speed of response which is very important for stock market trading. The developed trading system has medium frequency of deals submission that lets it to avoid expensive infrastructure which is usually needed in high-frequency trading systems. At the same time, the system uses the potential of high quality market microstructure data to the full extent. This paper describes the entire process of trading system development including feature engineering, models behavior comparison and creation of trading strategy with testing on historical data. На современных биржевых площадках, где большинство операций совершается посредством электронных транзакций, появляется возможность анализировать событийные микроструктурные рыночные данные. Они описывают физическое формирование цены на биржевые инструменты и обладают высокой информативностью, позволяя торговой системе находить сложные закономерности в поведении цены. Выявление таких закономерностей вручную является очень трудоемким процессом. Однако современные модели машинного обучения способны решать подобные задачи автоматически, подстраиваясь под постоянно меняющееся поведение рынка. В данной работе разрабатывается торговая система на базе модели машинного обучения и микроструктурных рыночных данных. Для исследования собрана информация с московской биржи ММВБ о событийных изменениях биржевой книги заявок и ленте всех сделок по ликвидному биржевому инструменту. Для моделирования используется модель логистической регрессии и ряд моделей искусственных нейронных сетей. Результаты исследования демонстрируют, что модель логистической регрессии не уступает в качестве прогнозирования более сложным моделям и при этом имеет высокую скорость формирования прогноза, что очень важно при принятии торгового решения на современных торговых площадках. Разработанная торговая система имеет среднюю частоту заключения сделок, что позволяет избежать дорогой инфраструктуры по сравнению с высокочастотной биржевой торговлей, но при этом дает возможность использовать весь потенциал высококачественных микроструктурных рыночных данных. Статья описывает все этапы построения торговой системы, включая выбор признаков для моделирования, сравнительный анализ моделей прогнозирования изменения цены, а также создание торгового алгоритма с тестированием на исторических данных. Он может применяться различными инвестиционными институтами для эффективного управления капиталом в биржевых торгах. Разработка более сложных и детальных торговых алгоритмов на базе модели машинного обучения позволит увеличить конечную эффективность всей торговой системы.

Suggested Citation

  • N. Bilev A. & Н. Билев А., 2018. "Моделирование изменения цены биржевого инструмента на базе микроструктурных рыночных данных // Modeling Stock Price Changes Based on Microstructural Market Data," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 22(5), pages 141-153.
  • Handle: RePEc:scn:financ:y:2018:i:5:p:141-153
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    References listed on IDEAS

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    1. Hoffmann, Peter, 2014. "A dynamic limit order market with fast and slow traders," Journal of Financial Economics, Elsevier, vol. 113(1), pages 156-169.
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